Deepo is a series of Docker images that

allows you to quickly set up your deep learning research environment
supports almost all commonly used deep learning frameworks
supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode
works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version)

and their Dockerfile generator that

allows you to customize your own environment with Lego-like modules
automatically resolves the dependencies for you

Quick Start

GPU Version

Installation

Step 1. Install Docker and nvidia-docker.

Step 2. Obtain the all-in-one image from Docker Hub

docker pull ufoym/deepo

For users in China who may suffer from slow speeds when pulling the image from the public Docker registry, you can pull deepo images from the China registry mirror by specifying the full path, including the registry, in your docker pull command, for example:

docker pull registry.docker-cn.com/ufoym/deepo

Usage

Now you can try this command:

docker run --runtime=nvidia --rm ufoym/deepo nvidia-smi

This should work and enables Deepo to use the GPU from inside a docker container. If this does not work, search the issues section on the nvidia-docker GitHub -- many solutions are already documented. To get an interactive shell to a container that will not be automatically deleted after you exit do

docker run --runtime=nvidia -it ufoym/deepo bash

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g.

docker run --runtime=nvidia -it -v /host/data:/data -v /host/config:/config ufoym/deepo bash

This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

Please note that some frameworks (e.g. PyTorch) use shared memory to share data between processes, so if multiprocessing is used the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to docker run.

docker run --runtime=nvidia -it --ipc=host ufoym/deepo bash

CPU Version

Installation

Step 1. Install Docker.

Step 2. Obtain the all-in-one image from Docker Hub

docker pull ufoym/deepo:cpu

Usage

Now you can try this command:

docker run -it ufoym/deepo:cpu bash

If you want to share your data and configurations between the host (your machine or VM) and the container in which you are using Deepo, use the -v option, e.g.

docker run -it -v /host/data:/data -v /host/config:/config ufoym/deepo:cpu bash

This will make /host/data from the host visible as /data in the container, and /host/config as /config. Such isolation reduces the chances of your containerized experiments overwriting or using wrong data.

Please note that some frameworks (e.g. PyTorch) use shared memory to share data between processes, so if multiprocessing is used the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to docker run.

docker run -it --ipc=host ufoym/deepo:cpu bash

You are now ready to begin your journey.

$ python

>>> import tensorflow
>>> import sonnet
>>> import torch
>>> import keras
>>> import mxnet
>>> import cntk
>>> import chainer
>>> import theano
>>> import lasagne
>>> import caffe
>>> import caffe2

$ caffe --version

caffe version 1.0.0

$ darknet

usage: darknet <function>

$ th

 │  ______             __   |  Torch7
 │ /_  __/__  ________/ /   |  Scientific computing for Lua.
 │  / / / _ \/ __/ __/ _ \  |  Type ? for help
 │ /_/  \___/_/  \__/_//_/  |  https://github.com/torch
 │                          |  http://torch.ch
 │
 │th>

Customization

Note that docker pull ufoym/deepo mentioned in Quick Start will give you a standard image containing all available deep learning frameworks. You can customize your own environment as well.

Unhappy with all-in-one solution?

If you prefer a specific framework rather than an all-in-one image, just append a tag with the name of the framework. Take tensorflow for example:

docker pull ufoym/deepo:tensorflow

Jupyter support

Step 1. pull the image with jupyter support

docker pull ufoym/deepo:all-jupyter

Step 2. run the image

docker run --runtime=nvidia -it -p 8888:8888 --ipc=host ufoym/deepo:all-jupyter jupyter notebook --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token= --notebook-dir='/root'

Build your own customized image with Lego-like modules

Step 1. prepare generator

git clone https://github.com/ufoym/deepo.git
cd deepo/generator

Step 2. generate your customized Dockerfile

For example, if you like pytorch and lasagne, then

python generate.py Dockerfile pytorch lasagne

This should generate a Dockerfile that contains everything for building pytorch and lasagne. Note that the generator can handle automatic dependency processing and topologically sort the lists. So you don't need to worry about missing dependencies and the list order.

You can also specify the version of Python:

python generate.py Dockerfile pytorch lasagne python==3.6

Step 3. build your Dockerfile

docker build -t my/deepo .

This may take several minutes as it compiles a few libraries from scratch.

Comparison to alternatives

.	modern-deep-learning	dl-docker	jupyter-deeplearning	Deepo
ubuntu	16.04	14.04	14.04	18.04
cuda	X	8.0	6.5-8.0	8.0-10.0/None
cudnn	X	v5	v2-5	v7
onnx	X	X	X	O
theano	X	O	O	O
tensorflow	O	O	O	O
sonnet	X	X	X	O
pytorch	X	X	X	O
keras	O	O	O	O
lasagne	X	O	O	O
mxnet	X	X	X	O
cntk	X	X	X	O
chainer	X	X	X	O
caffe	O	O	O	O
caffe2	X	X	X	O
torch	X	O	O	O
darknet	X	X	X	O

Tags

Available Tags

.	CUDA 10.0 / Python 3.6	CPU-only / Python 3.6
all-in-one	`latest` `all` `all-py36` `py36-cu100` `all-py36-cu100`	`all-py36-cpu` `all-cpu` `py36-cpu` `cpu`
all-in-one with jupyter	`all-jupyter-py36-cu100` `all-jupyter-py36` `all-jupyter`	`all-py36-jupyter-cpu` `py36-jupyter-cpu`
Theano	`theano-py36-cu100` `theano-py36` `theano`	`theano-py36-cpu` `theano-cpu`
TensorFlow	`tensorflow-py36-cu100` `tensorflow-py36` `tensorflow`	`tensorflow-py36-cpu` `tensorflow-cpu`
Sonnet	`sonnet-py36-cu100` `sonnet-py36` `sonnet`	`sonnet-py36-cpu` `sonnet-cpu`
PyTorch / Caffe2	`pytorch-py36-cu100` `pytorch-py36` `pytorch`	`pytorch-py36-cpu` `pytorch-cpu`
Keras	`keras-py36-cu100` `keras-py36` `keras`	`keras-py36-cpu` `keras-cpu`
Lasagne	`lasagne-py36-cu100` `lasagne-py36` `lasagne`	`lasagne-py36-cpu` `lasagne-cpu`
MXNet	`mxnet-py36-cu100` `mxnet-py36` `mxnet`	`mxnet-py36-cpu` `mxnet-cpu`
CNTK	`cntk-py36-cu100` `cntk-py36` `cntk`	`cntk-py36-cpu` `cntk-cpu`
Chainer	`chainer-py36-cu100` `chainer-py36` `chainer`	`chainer-py36-cpu` `chainer-cpu`
Caffe	`caffe-py36-cu100` `caffe-py36` `caffe`	`caffe-py36-cpu` `caffe-cpu`
Torch	`torch-cu100` `torch`	`torch-cpu`
Darknet	`darknet-cu100` `darknet`	`darknet-cpu`

Deprecated Tags

.	CUDA 9.0 / Python 3.6	CUDA 9.0 / Python 2.7	CPU-only / Python 3.6	CPU-only / Python 2.7
all-in-one	`py36-cu90` `all-py36-cu90`	`all-py27-cu90` `all-py27` `py27-cu90`		`all-py27-cpu` `py27-cpu`
all-in-one with jupyter	`all-jupyter-py36-cu90`	`all-py27-jupyter` `py27-jupyter`		`all-py27-jupyter-cpu` `py27-jupyter-cpu`
Theano	`theano-py36-cu90`	`theano-py27-cu90` `theano-py27`		`theano-py27-cpu`
TensorFlow	`tensorflow-py36-cu90`	`tensorflow-py27-cu90` `tensorflow-py27`		`tensorflow-py27-cpu`
Sonnet	`sonnet-py36-cu90`	`sonnet-py27-cu90` `sonnet-py27`		`sonnet-py27-cpu`
PyTorch	`pytorch-py36-cu90`	`pytorch-py27-cu90` `pytorch-py27`		`pytorch-py27-cpu`
Keras	`keras-py36-cu90`	`keras-py27-cu90` `keras-py27`		`keras-py27-cpu`
Lasagne	`lasagne-py36-cu90`	`lasagne-py27-cu90` `lasagne-py27`		`lasagne-py27-cpu`
MXNet	`mxnet-py36-cu90`	`mxnet-py27-cu90` `mxnet-py27`		`mxnet-py27-cpu`
CNTK	`cntk-py36-cu90`	`cntk-py27-cu90` `cntk-py27`		`cntk-py27-cpu`
Chainer	`chainer-py36-cu90`	`chainer-py27-cu90` `chainer-py27`		`chainer-py27-cpu`
Caffe	`caffe-py36-cu90`	`caffe-py27-cu90` `caffe-py27`		`caffe-py27-cpu`
Caffe2	`caffe2-py36-cu90` `caffe2-py36` `caffe2`	`caffe2-py27-cu90` `caffe2-py27`	`caffe2-py36-cpu` `caffe2-cpu`	`caffe2-py27-cpu`
Torch	`torch-cu90`	`torch-cu90` `torch`		`torch-cpu`
Darknet	`darknet-cu90`	`darknet-cu90` `darknet`		`darknet-cpu`

Citation

@misc{ming2017deepo,
    author = {Ming Yang},
    title = {Deepo: set up deep learning environment in a single command line.},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/ufoym/deepo}}
}

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Licensing

Deepo is MIT licensed.

About

Set up deep learning environment in a single command line.

http://ufoym.com/deepo

MIT License

Languages

Language:Python 88.9%Language:Shell 11.1%

AuroAi / deepo

Table of contents

Quick Start

GPU Version

Installation

Step 1. Install Docker and nvidia-docker.

Step 2. Obtain the all-in-one image from Docker Hub

Usage

CPU Version

Installation

Step 1. Install Docker.

Step 2. Obtain the all-in-one image from Docker Hub

Usage

Customization

Unhappy with all-in-one solution?

Jupyter support

Step 1. pull the image with jupyter support

Step 2. run the image

Build your own customized image with Lego-like modules

Step 1. prepare generator

Step 2. generate your customized Dockerfile

Step 3. build your Dockerfile

Comparison to alternatives

Tags

Available Tags

Deprecated Tags

Citation

Contributing

Licensing

About

Languages